English

A CLT for empirical processes involving time-dependent data

Probability 2013-03-18 v2 Statistics Theory Statistics Theory

Abstract

For stochastic processes {Xt:tE}\{X_t:t\in E\}, we establish sufficient conditions for the empirical process based on {IXtyPr(Xty):tE,yR}\{I_{X_t\le y}-\operatorname{Pr}(X_t\le y):t\in E,y\in\mathbb{R}\} to satisfy the CLT uniformly in tE,yRt\in E,y\in\mathbb{R}. Corollaries of our main result include examples of classical processes where the CLT holds, and we also show that it fails for Brownian motion tied down at zero and E=[0,1]E=[0,1].

Keywords

Cite

@article{arxiv.1008.2697,
  title  = {A CLT for empirical processes involving time-dependent data},
  author = {James Kuelbs and Thomas Kurtz and Joel Zinn},
  journal= {arXiv preprint arXiv:1008.2697},
  year   = {2013}
}

Comments

Published in at http://dx.doi.org/10.1214/11-AOP711 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org)

R2 v1 2026-06-21T16:01:23.583Z